Predictively provisioning cloud computing resources for virtual machines

ABSTRACT

Methods, computer program products, and systems are presented. The methods include, for instance: predictively provisioning, by one or more processor, cloud computing resources of a cloud computing environment for at least one virtual machine; and initializing, by the one or more processor, the at least one virtual machine with the provisioned cloud computing resources of the cloud computing environment. In one embodiment, the predictively provisioning may include: receiving historical utilization information of multiple virtual machines of the cloud computing environment, the multiple virtual machines having similar characteristics to the at least one virtual machine; and determining the cloud computing resources for the at least one virtual machine using the historical utilization information of the multiple virtual machines. In another embodiment, the predictively may include updating a provisioning database with the historical utilization information of the multiple virtual machines of the cloud computing environment.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. application Ser. No.16/057,885, filed Aug. 8, 2018, entitled “Predictively ProvisioningCloud Computing Resources for Virtual Machines”, which is incorporatedby reference herein in its entirety, which is a continuation of U.S.application Ser. No. 15/692,618, filed Aug. 31, 2017, entitled“Predictively Provisioning Cloud Computing Resources for VirtualMachines,” which is incorporated by reference herein in its entirety,which is a continuation of U.S. application Ser. No. 14/852,043, filedSep. 11, 2015 entitled “Predictively Provisioning Cloud ComputingResources for Virtual Machines”, which is incorporated by referenceherein in its entirety.

TECHNICAL FIELD

The present disclosure relates to virtualization technologies, includingvirtual networking and virtual computing, and more particularly tomethods, computer program products, and systems for predictivelyprovisioning cloud computing resources of a cloud computing environmentfor virtual machines.

BACKGROUND

To meet the continuing demand for high-capacity distributed data centersand the increasing need for scalable computing resources, the computerindustry has pursued technologies to enable large scale deployments ofcloud computing environments. Cloud computing environments, which makeuse of virtualization technologies, such as virtual networking andvirtual machine architectures, provide enhanced flexibility in thedeployment of services and hardware infrastructures, by allowing for theabstraction of physical resources into logical representations. However,some of these attractive aspects of cloud computing environments canpose management challenges, for example, in the areas of provisioning,monitoring, and deploying such resources, because the very abstractionsthat provide flexibility can impede manageability.

By way of example, a large-scale cloud computing environment may includenumerous geographically dispersed computing nodes hosting numerouscomputing resources for numerous different tenants. For instance,computing equipment and systems from multiple vendors can be installed,and management, provisioning, and deployment of cloud computingresources may be performed on an ad hoc basis, without centralizedmanagement and/or control. Such conditions can lead to poor allocationof resources, in which some computing nodes are running at fullcapacity, and others are idling.

For example, current systems for choosing provisioning locations ofvirtual machines in a cloud computing environment only focus on staticsystem information available at the time a virtual machine isprovisioned, and do not take into account varying run-time resourceutilization of virtual machines when allocating cloud computingresources. However, because each virtual machine may run for a longperiod of time, and during such time, may allocate and re-allocate cloudcomputing resources, initial static allocations can lead to widedepartures from optimized load balancing of resources in a complex,distributed cloud computing environment.

In addition, multiple hypervisors, or virtual machine monitors, may eachcontrol the deployment of virtual machines across different computingnodes, based in part on software compatibility of the hypervisors andthe computing nodes. In such a case, achieving a scalable, load-balanceddeployment of numerous virtual machines is hampered because of the lackof multi-vendor interoperability and ability to allocate resources on aunified basis regardless of which hypervisor controls such resources.

Further, geographic dispersal of computing resources, varying levels ofnetwork bandwidth, disparate levels of processing resources, and thepresence of multiple tenants on a given system, can lead to an extremelycomplex deployment model that is far beyond the ability of present toolsand techniques to optimize and/or manage. Therefore, a need exists formethods, computer program products, and systems to intelligentlyprovision cloud computing resources for virtual machines.

SUMMARY

The shortcomings of the prior art are overcome, and additionaladvantages are provided, through the provision, in one aspect, of amethod. The method includes, for example: predictively provisioning, byone or more processor, cloud computing resources of a cloud computingenvironment for at least one virtual machine; and triggering, by the oneor more processor, initializing the at least one virtual machine withthe provisioned cloud computing resources of the cloud computingenvironment. For example, the method may optimize the use of cloudcomputing resources.

In one embodiment, the predictively provisioning includes: receivinghistorical utilization information of multiple virtual machines of thecloud computing environment, the multiple virtual machines havingsimilar characteristics to the at least one virtual machine; anddetermining the cloud computing resources for the at least one virtualmachine using the historical utilization information of the multiplevirtual machines. For example, historical utilization information may beused to predict the expected runtime resource utilization of a virtualmachine with similar characteristics, minimizing the need to migrate orre-provision the virtual machine as resource utilization changes.

In one embodiment, the predictively provisioning further includesupdating a provisioning database with the historical utilizationinformation of the multiple virtual machines of the cloud computingenvironment. For example, a provisioning database may be used to storelong-term historical utilization information of virtual machines toallow provisioning to be continuously improved during operation of acloud computing environment.

In one embodiment, the determining further includes data mining theprovisioning database to determine the cloud computing resources, thedata mining including using at least one of regression or constraintanalysis of the provisioning database. For example, data miningtechniques, including regression or constraint analysis, may allow longterm trends of virtual machine performance attributes to be used indetermining which cloud computing resources to provision for a virtualmachine.

In one embodiment, the predictively provisioning includes: receiving aprovisioning request, the provisioning request including at leastprocessor, memory, storage, or tenant requirements of the at least onevirtual machine; and comparing the processor, memory, storage, or tenantrequirements of the at least one virtual machine with availableresources of the cloud computing environment to provision the cloudcomputing resources for the at least one virtual machine. For example, aprovisioning request may include target resource level requirements of avirtual machine to facilitate identification of cloud computingresources for the virtual machine.

In one embodiment, the cloud computing environment includes multiplehypervisors, the predictively provisioning includes selecting the cloudcomputing resources associated with a hypervisor of the multiplehypervisors, and the triggering initializing includes sending aninitialization request to the selected hypervisor. For example, aprovisioning method that allows for operation with multiple hypervisorsmay allow for a mixed-vendor cloud computing environment to be deployedand provide compatibility between the different hypervisors.

In one embodiment, the predictively provisioning includes: calculatingavailable cloud computing resources of the cloud computing environment;filtering the available cloud computing resources to determine the cloudcomputing resources for the at least one virtual machine, the filteringincluding using at least one of real-time data, empirical data, orprediction data of the cloud computing environment. For example,real-time data can allow for more accurate provisioning based on currentactual conditions of a cloud computing environment, and empirical and/orprediction data can make use of computational or other models of virtualmachine behavior during early stages of operation of a cloud computingenvironment (e.g., before collecting historical data).

In one embodiment, the triggering initializing includes hosting the atleast one virtual machine using the provisioned cloud computingresources of the cloud computing environment. For example, immediatelyafter provisioning, resources may be allocated from a target cloudcomputing host to host the virtual machine using the appropriate cloudcomputing resources.

In another aspect, a computer program product is provided. The computerprogram product includes a computer readable storage medium readable byone or more processor and storing instructions for execution by the oneor more processor for performing a method. The method includes, forexample: predictively provisioning, by one or more processor, cloudcomputing resources of a cloud computing environment for at least onevirtual machine; and triggering, by the one or more processor,initializing the at least one virtual machine with the provisioned cloudcomputing resources of the cloud computing environment. For example, themethod may optimize the use of cloud computing resources.

In a further aspect, a system is provided. The system includes, forinstance a memory. In addition, the system includes one or moreprocessor in communication with the memory. Further, the system includesprogram instructions executable by the one or more processor via thememory to perform a method. The method includes, for example:predictively provisioning, by one or more processor, cloud computingresources of a cloud computing environment for at least one virtualmachine; and triggering, by the one or more processor, initializing theat least one virtual machine with the provisioned cloud computingresources of the cloud computing environment. For example, the methodmay optimize the use of cloud computing resources.

Additional features and advantages are realized through the techniquesset forth herein. Other embodiments and aspects are described in detailherein and are considered a part of the claimed invention.

BRIEF DESCRIPTION OF THE DRAWINGS

One or more aspects of the present invention are particularly pointedout and distinctly claimed as examples in the claims at the conclusionof the specification. The foregoing and other objects, features, andadvantages of the invention are apparent from the following detaileddescription taken in conjunction with the accompanying drawings inwhich:

FIG. 1 depicts a cloud computing node according to an embodiment of thepresent invention;

FIG. 2 depicts a cloud computing environment according to an embodimentof the present invention;

FIG. 3 depicts abstraction model layers according to an embodiment ofthe present invention;

FIG. 4 depicts a hardware overview of a computing node, in accordancewith one or more aspects set forth herein;

FIG. 5 is an exemplary block diagram of a system, in accordance with oneor more aspects set forth herein;

FIG. 6 depicts one or more embodiments of a process for predictivelyprovisioning cloud computing resources for a virtual machine, inaccordance with one or more aspects set forth herein; and

FIGS. 7A-7C are diagrams illustrating further aspects of a process forpredictively provisioning cloud computing resources for a virtualmachine, in accordance with one or more aspects set forth herein.

DETAILED DESCRIPTION

Aspects of the present disclosure and certain features, advantages, anddetails thereof, are explained more fully below with reference to thenon-limiting examples illustrated in the accompanying drawings.Descriptions of well-known materials, fabrication tools, processingtechniques, etc., are omitted so as not to unnecessarily obscure thedisclosure in detail. It should be understood, however, that thedetailed description and the specific examples, while indicating aspectsof the invention, are given by way of illustration only, and not by wayof limitation. Various substitutions, modifications, additions, and/orarrangements, within the spirit and/or scope of the underlying inventiveconcepts will be apparent to those skilled in the art from thisdisclosure.

The present disclosure provides, in part, methods, computer programproducts, systems, network devices, and virtual machine managementsoftware for predictively provisioning cloud computing resources forvirtual machines.

Virtualization technologies continue to be deployed to meet consumerdemand for high-capacity, flexible data center services, includingmulti-tenant solutions that allow computing resources of numeroustenants, or customers, to be hosted within a cloud environment. In sucha case, the flexible virtualized technologies pose managementchallenges, for example, in the deployment and provisioning processesfor virtual machines.

For instance, during operation of a cloud computing infrastructure,virtual machines will be launched on a routine basis. Because a cloudcomputing infrastructure may span numerous data centers spread acrossdifferent geographical regions, it is important to provision and launchvirtual machines in such a way that network bandwidth is optimized.

In addition, some virtual machines, such as those running transactionprocessing web servers, may have different growth and performancerequirements from other virtual machines, such as those running computeraided design programs. Because of these different characteristics, itcan be advantageous to allocate resources in a manner that takes intoaccount various characteristics that are unique to different types ofvirtual machines before provisioning resources for such machines.Further, multi-vendor infrastructures may also lead to problems inresource allocation, because a single virtual machine monitor orhypervisor may not be able to manage all resources, and resources may besubdivided by vendor, partially defeating some of the goals ofvirtualization.

Advantageously, the techniques disclosed herein allow for optimizedprovisioning of cloud computing resources so that resources may beefficiently used, avoiding, for example, situations in which some cloudcomputing resources are idling and other cloud computing resources areover-subscribed.

Reference is made below to the drawings, which are not drawn to scalefor ease of understanding, wherein the same reference numbers usedthroughout different figures designate the same or similar components.

FIGS. 1-4 depict various aspects of computing, including cloudcomputing, in accordance with one or more aspects set forth herein.

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 1, a schematic of an example of a cloud computingnode is shown. Cloud computing node 10 is only one example of a suitablecloud computing node and is not intended to suggest any limitation as tothe scope of use or functionality of embodiments of the inventiondescribed herein. Regardless, cloud computing node 10 is capable ofbeing implemented and/or performing any of the functionality set forthhereinabove.

In cloud computing node 10 there is a computer system/server 12, whichis operational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 12 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, hand-held or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

Computer system/server 12 may be described in the general context ofcomputer system-executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 12 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 1, computer system/server 12 in cloud computing node 10is shown in the form of a general-purpose computing device. Thecomponents of computer system/server 12 may include, but are not limitedto, one or more processors or processing units 16, a system memory 28,and a bus 18 that couples various system components including systemmemory 28 to processor 16.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnects (PCI) bus.

Computer system/server 12 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 12, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system/server 12 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As will be further depicted and described below,memory 28 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions of embodiments of the invention.

Program/utility 40, having a set (at least one) of program modules 42,may be stored in memory 28 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 42 generally carry out the functions and/ormethodologies of embodiments of the invention as described herein.

Computer system/server 12 may also communicate with one or more externaldevices 14 such as a keyboard, a pointing device, a display 24, etc.;one or more devices that enable a user to interact with computersystem/server 12; and/or any devices (e.g., network card, modem, etc.)that enable computer system/server 12 to communicate with one or moreother computing devices. Such communication can occur via Input/Output(I/O) interfaces 22. Still yet, computer system/server 12 cancommunicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 20. As depicted, network adapter 20communicates with the other components of computer system/server 12 viabus 18. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 12. Examples, include, but are not limited to: microcode,device drivers, redundant processing units, external disk drive arrays,RAID systems, tape drives, and data archival storage systems, etc.

Referring now to FIG. 2, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 2 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 3, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 2) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 3 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and predictively provisioning cloud computingresources for virtual machines 96 as described herein.

FIG. 4 depicts a hardware overview of a computing node 10, which may bea cloud computing node, in accordance with one or more aspects set forthherein. By way of example, computing node 10 may generally be any of thecomputing devices described herein, such as network devices, clientcomputers, server computers, etc.

Program/utility 40 as set forth in FIG. 1 can provide the functionalityof predictively provisioning cloud computing resources for virtualmachines 96 as set forth in FIG. 3. Program/utility 40 as set forth inFIG. 1 can include one or more program 440 as set forth in FIG. 4, andprogram/utility 40 as set forth in FIG. 1 can optionally include some orall of one or more program 441, 442, 443, 444, 445.

One or more program 440 can have a set (at least one) of programmodules, and may be stored in memory 28 by way of example, and notlimitation, as well as an operating system, one or more applicationprograms, other program modules, and program data. Each of the operatingsystem, one or more application programs, other program modules, programdata, and one or more program, or some combination thereof, may includean implementation of a networking environment. One or more program 440(and optionally at least one of one or more program 441, 442, 443, 444,445) generally carry out the functions and/or methodologies ofembodiments of the invention as described herein, such as predictivelyprovisioning cloud computing resources for virtual machines 96 (FIG. 3).

Referring again to FIG. 4:

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

FIG. 5 is an exemplary block diagram of a system 500, in accordance withone or more aspects set forth herein. In the embodiment of FIG. 5,system 500 includes numerous devices, which may be or include computingnodes 10 as previously described, connected by a network 501. Forexample, network 501 may be a physical network or a virtual network. Aphysical network can be, for example, a physical telecommunicationsnetwork connecting numerous computer nodes or systems, such as computerservers and computer clients. By contrast a virtual network can, forexample, combine numerous physical networks or parts thereof into alogical virtual network. In another example, numerous virtual networkscan be defined over a single physical network.

By way of explanation, FIG. 5 depicts an example environment in whichone or more requesting nodes 540 make a request for provisioning cloudcomputing resources, for example, so that a virtual machine may beinitialized using those resources. In one example, a provisioning engine550 performs techniques described herein to allocate resources. In sucha case, provisioning engine 550 may trigger initialization of a virtualmachine, e.g., by sending a message to a hosting computing node, whichin turn will lead to the hosting computing node initializing the virtualmachine. Such resources may be located in different regions, such asregion A or region B. Different reasons may represent differentgeographic locations or different data centers or devices in a givengeographical region.

In one embodiment, the different regions may include one or morecomputing nodes 510, which may have the characteristics of computingnodes 10 described in FIG. 4. For example, each computing node 510 maybe capable of hosting or running numerous virtual machines. In addition,computing nodes 510 may be connected via storage area network switches520 to storage nodes 530.

FIG. 6 depicts embodiments of a process for predictively provisioningcloud computing resources for virtual machines, in accordance with oneor more aspects set forth herein. By way of example, the processesdescribed with respect to FIG. 6 can be performed using one or moreprogram 440 on one or more device 550 (FIG. 5), as detailed with respectto FIG. 4.

In the embodiment of FIG. 6, one or more program 440 at block 610predictively provisions cloud computing resources of a cloud computingenvironment for at least one virtual machine; and one or more program440 at block 620 triggers initialization of the at least one virtualmachine with the provisioned cloud computing resources of the cloudcomputing environment. For example, one or more program 440 at block 620triggers initialization by transmitting a message to a provisioned cloudcomputing resource, such as a hosting computing node, requesting thatthe provisioned cloud computing resource perform initialization of thevirtual machine.

In one embodiment, one or more program 440 at block 610 receiveshistorical utilization information of multiple virtual machines of thecloud computing environment, the multiple virtual machines havingsimilar characteristics to the at least one virtual machine; and one ormore program 440 at block 610 determines the cloud computing resourcesfor the at least one virtual machine using the historical utilizationinformation of the multiple virtual machines.

In one embodiment, one or more program 440 at block 610 updates aprovisioning database with the historical utilization information of themultiple virtual machines of the cloud computing environment.

In one embodiment, one or more program 440 at block 610 data mines theprovisioning database to determine the cloud computing resources, thedata mining including using at least one of regression or constraintanalysis of the provisioning database.

In one embodiment, one or more program 440 at block 610 receives aprovisioning request, the provisioning request including at leastprocessor, memory, storage, or tenant requirements of the at least onevirtual machine; and one or more program 440 at block 610 compares theprocessor, memory, storage, or tenant requirements of the at least onevirtual machine with available resources of the cloud computingenvironment to provision the cloud computing resources for the at leastone virtual machine.

In one embodiment, the cloud computing environment includes multiplehypervisors. In such a case, one or more program 440 at block 610selects the cloud computing resources associated with a hypervisor ofthe multiple hypervisors, and one or more program 440 at block 620 sendsan initialization request to the selected hypervisor.

In one embodiment, one or more program 440 at block 610 calculatesavailable cloud computing resources of the cloud computing environment;and one or more program 440 at block 610 filters the available cloudcomputing resources to determine the cloud computing resources for theat least one virtual machine, the filtering including using at least oneof real-time data, empirical data, or prediction data of the cloudcomputing environment.

In one embodiment, one or more program 440 at block 620 hosts the atleast one virtual machine using the provisioned cloud computingresources of the cloud computing environment.

FIGS. 7A-7C are diagrams illustrating further aspects of a process forpredictively provisioning cloud computing resources for a virtualmachine, in accordance with one or more aspects set forth herein. By wayof explanation, in FIGS. 7A-7C, processes are illustrated from the pointof view of a provisioning engine one or more program 440 (e.g., runningon provisioning engine 550 of FIG. 5), a requesting node one or moreprogram 441 (e.g., running on requesting node 540 of FIG. 5), a region Aone or more program 442 (e.g., running on a computing node of region Aof FIG. 5), and a region B one or more program 443 (e.g., running on acomputing node of region B of FIG. 5). In addition, one or more program440 at block 610 (FIG. 6) predictively provisioning can include one ormore program 440-443 performing one or more of blocks 610 a-610 w (FIGS.7A-7C). Further, one or more program 440 at block 620 (FIG. 6)triggering initializing can include one or more program 440-443performing one or more of blocks 620 a-620 d (FIGS. 7A-7C).

In one or more embodiments, some or all of the programs 440-443 may runon a different collection of physical or virtual machines or processors,depending on the need for scalability of the system. In one specificexample, all of the programs 440-443 could run on a singlemulti-processor server system. In another specific example, variousportions of provisioning engine one or more program 440 may run ondifferent processors running on different computing nodes.

By way of overview, FIG. 7A illustrates, at least in part, one or moreembodiments in which a provisioning request is sent from a requestingnode to a provisioning engine, and the provisioning engine responds tothe request by provisioning cloud computing resources of a cloudcomputing environment for a virtual machine. In addition, FIG. 7Billustrates, at least in part, one or more embodiments in which aprovisioning engine, over the course of time, gathers information anddata, such as utilization information of various virtual machines, anduses that information and data, along with various analyticaltechniques, described further below, to build up database(s) ofinformation that may be used to respond to subsequent provisioningrequests. In such a manner, the present techniques allow for predictiveprovisioning of cloud computing resources to best optimize theperformance and utilization of a cloud computing environment.

With reference to FIG. 7A, in one embodiment, region A one or moreprogram 442 at block 610 a sends virtual machine VM1 utilizationinformation to provisioning engine one or more program 440. In addition,in one embodiment, region B one or more program 443 at block 610 b sendsvirtual machine VM2 utilization information to provisioning engine oneor more program 440. Further, in one embodiment, region A one or moreprogram 442 at block 610 c sends virtual machine VM3 utilizationinformation to provisioning engine one or more program 440.

In one or more embodiments, provisioning engine one or more program 440at block 610 d receives virtual machine utilization information relatedto virtual machines VM1-VM3, and at block 610 e updates a provisioningdatabase in accordance with the received information. For example,virtual machine VM1 may be a first type of virtual machine, such as aweb server, virtual machine VM2 may be a second type of virtual machine,such as a hosted application server, and virtual machine VM3 may be athird type of virtual machine, such as a transaction processing server.In such a case, provisioning engine one or more program 440 may receiveinformation about each of virtual machines VM1-VM3, and store suchinformation for subsequent processing.

In operation of a system according to the present technique, informationof numerous running virtual machines may be received by the provisioningengine, which may store the information within a database, and performdata analysis, including regression analysis, time series analytics,etc., to find patterns in the data which can be used to informsubsequent provisioning decisions. For example, the provisioning enginemay receive information over time that indicates that certain virtualmachines, for example intended to run certain server programs, may havea growth rate that requires a specific amount of RAM, disk space, CPUresources, etc. In such an example, the provisioning engine can storeinstructions in a database to indicate that a future provisioningrequest for a virtual machine running the same or similar server programshould be responded to with an amount of RAM, disk, and CPU that isbased on historical norms previously determined for such serverprograms.

Continuing with reference to FIG. 7A, in one or more embodiments, arequesting node one or more program 441 at block 610 f sends aprovisioning request to provisioning engine one or more program 440. Insuch a case, for example, provisioning engine one or more program 440 atblock 610 g receives the provisioning request. In one embodiment,provisioning engine one or more program 440 at block 610 h calculatesavailable resources, e.g., resources of a cloud computing environment.For example, the provisioning engine can determine all availableresources of the cloud computing environment that are not currentlyallocated to other virtual machines. In one embodiment, provisioningengine one or more program 440 at block 610 i may filter resources usingdata from the provisioning request. For example, the provisioning enginemay use real-time data, empirical data, prediction data, or acombination thereof to provision appropriate available resources.

In one or more embodiments, provisioning engine one or more program 440at block 620 a may next determine the appropriate cloud computingresources for the virtual machine, responsive to the provisioningrequest. In such a case, the provisioning engine may make use of thehistorical utilization information of multiple previously operatingvirtual machines that was gathered by the provisioning engine, andallocate resources for virtual machine VM4. In one example, region Anode one or more program 442 at block 620 b may then launch and hostvirtual machine VM4.

With reference to FIG. 7B, in one embodiment, region A one or moreprogram 442 at block 610 j sends virtual machine VM4 utilizationinformation to provisioning engine one or more program 440. In addition,in one embodiment, region B one or more program 443 at block 610 k sendsvirtual machine VM6 utilization information to provisioning engine oneor more program 440. Further, in one embodiment, region A one or moreprogram 442 at block 610 l sends virtual machine VM5 utilizationinformation to provisioning engine one or more program 440.

In one or more embodiments, provisioning engine one or more program 440at block 610 m receives virtual machine utilization information relatedto virtual machines VM4-VM6, and at block 610 n updates a provisioningdatabase in accordance with the received information.

In one embodiment, provisioning engine one or more program 440 at block610 o data mines the provisioning database to determine the cloudcomputing resources. For example, the provisioning engine may useregression analysis to calculate trends based on the receivedutilization information, and store those trends in the database for usein a subsequent provisioning request. In another embodiment,provisioning engine one or more program 440 at block 610 p data minesthe provisioning database using constraint analysis of the provisioningdatabase. For example, a multi-dimensional constraint problem may besolved to optimally determine which cloud computing resources, such asRAM, disks, and CPU resources should be allocated together as a unit toprovision a virtual machine.

In one or more embodiments, a requesting node one or more program 441 atblock 610 q sends a provisioning request to provisioning engine one ormore program 440. In such a case, for example, provisioning engine oneor more program 440 at block 610 r filters the available cloud computingresources to determine the cloud computing resources to allocate for avirtual machine VM7.

In one or more embodiments, provisioning engine one or more program 440at block 620 c may next determine the appropriate cloud computingresources for the virtual machine, responsive to the provisioningrequest. In one example, region B one or more program 443 at block 620 dmay then launch and host virtual machine VM7.

With reference to FIG. 7C, in one or more embodiments, on an ongoingbasis, each of regions A-B may send a variety of information toprovisioning engine one or more program 440 in order to captureutilization trends to enhance provisioning of cloud computing resourcesfor virtual machines. By way of example, region A one or more program442 at block 610 s (and/or region B one or more program 443 at block 610t) may send CPU, memory, and disk utilization information toprovisioning engine one or more program 440. For instance, a table withreal-time CPU, memory, and disk utilization for each hosted virtualmachine may be sent to the provisioning engine on a periodic basis.

In one example, provisioning engine one or more program 440 at block 610u can received such a table of utilization information, and at block 610v update a provisioning database appropriately. For example, theprovisioning database may contain CPU, memory, and disk utilization foreach hosted virtual machine, correlated to a virtual machineidentification (ID) number. In such a case, the provisioning databasemay contain a list of attributes corresponding to the virtual machine IDnumber, such as running operating system, application programs, numberof client connections, tenant ID, and so forth.

In one or more embodiments, provisioning engine one or more program 440at block 610 w can perform ongoing analytics and use such information topredict the behavior of virtual machines, including the expected rate atwhich various resources may be allocated and released. For instance, theprovisioning engine may determine that some types of virtual machinesare relatively CPU intensive, but use relatively little disk resources,and other types of virtual machines have growing needs for diskutilization as time goes on. In such cases, the provisioning engine canmake more optimized choices for provisioning a future virtual machine bymatching its predicted resource needs with a hosting region that hascurrent available capacity of such resources.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting. As used herein, thesingular forms “a,” “an,” and “the” are intended to include the pluralforms as well, unless the context clearly indicates otherwise. It willbe further understood that the terms “comprise” (and any form ofcomprise, such as “comprises” and “comprising”), “have” (and any form ofhave, such as “has” and “having”), “include” (and any form of include,such as “includes” and “including”), and “contain” (and any form ofcontain, such as “contains” and “containing”) are open-ended linkingverbs. As a result, a method or device that “comprises,” “has,”“includes,” or “contains” one or more steps or elements possesses thoseone or more steps or elements, but is not limited to possessing onlythose one or more steps or elements. Likewise, a step of a method or anelement of a device that “comprises,” “has,” “includes,” or “contains”one or more features possesses those one or more features, but is notlimited to possessing only those one or more features. Furthermore, adevice or structure that is configured in a certain way is configured inat least that way, but may also be configured in ways that are notlisted.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below, if any, areintended to include any structure, material, or act for performing thefunction in combination with other claimed elements as specificallyclaimed. The description set forth herein has been presented forpurposes of illustration and description, but is not intended to beexhaustive or limited to the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the disclosure. Theembodiment was chosen and described in order to best explain theprinciples of one or more aspects set forth herein and the practicalapplication, and to enable others of ordinary skill in the art tounderstand one or more aspects as described herein for variousembodiments with various modifications as are suited to the particularuse contemplated.

What is claimed is:
 1. A computer program product comprising: a computerreadable storage medium readable by one or more processor and storinginstructions for execution by the one or more processor for performing amethod comprising: predictively provisioning cloud computing resourcesof a cloud computing environment for at least one virtual machine; andtriggering initializing the at least one virtual machine with theprovisioned cloud computing resources of the cloud computingenvironment, wherein the predictively provisioning includes receivinghistorical cloud computing resource utilization information of multiplevirtual machines of the cloud computing environment, and determining thecloud computing resources for the at least one virtual machine using thehistorical cloud computing resource utilization information of themultiple virtual machines, wherein the predictively provisioning cloudcomputing resources of a cloud computing environment for at least onevirtual machine includes predictively provisioning a certain virtualmachine for running a certain program, wherein the receiving historicalcloud computing resource utilization information of multiple virtualmachines of the cloud computing environment includes receivinghistorical cloud computing resource utilization information of multiplevirtual machines running the certain program.
 2. The computer programproduct of claim 1, wherein the determining the cloud computingresources for the at least one virtual machine includes determiningprovisioning resources for the certain virtual machine, wherein thetriggering initializing the at least one virtual machine includestriggering initializing the certain virtual machine with theprovisioning resources for the certain virtual machine.
 3. The computerprogram product of claim 1, wherein the determining the cloud computingresources for the at least one virtual machine includes determiningprovisioning resources for the certain virtual machine, wherein thetriggering initializing the at least one virtual machine includestriggering initializing the certain virtual machine with theprovisioning resources for the certain virtual machine.
 4. The computerprogram product of claim 1, wherein the triggering initializing the atleast one virtual machine includes triggering initializing the certainvirtual machine with the provisioning resources for the certain virtualmachine, wherein determining provisioning resources for the certainvirtual machine includes determining predicted disk utilization resourceneeds of the certain virtual machine based on historical diskutilization of the multiple virtual machines running the certainprogram.
 5. A system comprising: a memory; one or more processor incommunication with the memory; and program instructions executable bythe one or more processor via the memory to perform a method, the methodcomprising: predictively provisioning cloud computing resources of acloud computing environment for at least one virtual machine; andtriggering initializing the at least one virtual machine with theprovisioned cloud computing resources of the cloud computingenvironment, wherein the predictively provisioning includes receivingand processing historical utilization information of multiple virtualmachines of the cloud computing environment, wherein receivinghistorical utilization information of multiple virtual machines of thecloud computing environment includes receiving and processing historicalcloud computing resource utilization information of multiple virtualmachines of a certain type, wherein the predictively provisioning cloudcomputing resources of a cloud computing environment for at least onevirtual machine includes predictively provisioning a certain virtualmachine of a certain type, wherein determining provisioning resourcesfor the certain virtual machine includes determining predicted diskutilization resource needs of the certain virtual machine usinghistorical disk utilization trends of the multiple virtual machines ofthe certain type.
 6. The system of claim 5, wherein the triggeringinitializing the at least one virtual machine includes triggeringinitializing the certain virtual machine with the provisioning resourcesfor the certain virtual machine, wherein the determining provisioningresources for the certain virtual machine includes determining predicteddisk utilization resource needs of the certain virtual machine based ona growth rate of the certain virtual machine determined using ahistorical disk utilization growth rate of the multiple virtual machinesof the certain type.
 7. The system of claim 5, wherein the determiningprovisioning resources for the certain virtual machine includesdetermining predicted disk utilization resource needs of the certainvirtual machine based on a growth rate of the certain virtual machinedetermined using a historical disk utilization growth rate of themultiple virtual machines of the certain type.
 8. The system of claim 5,wherein the triggering initializing the at least one virtual machineincludes triggering initializing the certain virtual machine with theprovisioning resources for the certain virtual machine.
 9. A computerimplemented method comprising: predictively provisioning cloud computingresources of a cloud computing environment for at least one virtualmachine; and triggering initializing the at least one virtual machinewith the provisioned cloud computing resources of the cloud computingenvironment, wherein the predictively provisioning includes receivinghistorical utilization information of multiple virtual machines of thecloud computing environment, and determining the cloud computingresources for the at least one virtual machine using the historicalutilization information of the multiple virtual machines, wherein thepredictively provisioning cloud computing resources of a cloud computingenvironment for at least one virtual machine includes predictivelyprovisioning a certain virtual machine running a certain program,wherein determining provisioning resources for the certain virtualmachine includes determining predicted resource needs of the certainvirtual machine based on a growth rate of the certain virtual machinedetermined using a historical growth rate of machines of the multiplevirtual machines running the certain program.
 10. The computerimplemented method of claim 9, wherein the triggering initializing theat least one virtual machine includes triggering initializing thecertain virtual machine with the provisioning resources for the certainvirtual machine, wherein the determining provisioning resources for thecertain virtual machine includes determining predicted disk utilizationresource needs of the certain virtual machine based on a growth rate ofthe certain virtual machine determined using a historical diskutilization growth rate of the multiple virtual machines running thecertain program.
 11. The computer implemented method of claim 9, whereinthe triggering initializing the at least one virtual machine includestriggering initializing the certain virtual machine with theprovisioning resources for the certain virtual machine, wherein thedetermining provisioning resources for the certain virtual machineincludes determining predicted resource utilization needs of the certainvirtual machine based on historical resource utilization of the multiplevirtual machines running the certain program.
 12. The computerimplemented method of claim 9, wherein the triggering initializing theat least one virtual machine includes triggering initializing thecertain virtual machine with the provisioning resources for the certainvirtual machine, wherein the determining provisioning resources for thecertain virtual machine includes determining predicted resourceutilization needs of the certain virtual machine based on historicalresource utilization of the multiple virtual machines.
 13. The computerimplemented method of claim 9, wherein the triggering initializing theat least one virtual machine includes triggering initializing thecertain virtual machine with the provisioning resources for the certainvirtual machine.
 14. The computer implemented method of claim 9, whereinthe determining provisioning resources for the certain virtual machineincludes determining predicted resource utilization needs of the certainvirtual machine based on historical resource utilization of the multiplevirtual machines running the certain program.
 15. The computerimplemented method of claim 9, wherein the determining provisioningresources for the certain virtual machine includes determining predicteddisk utilization resource needs of the certain virtual machine based ona growth rate of the certain virtual machine determined using ahistorical disk utilization growth rate of the multiple virtual machinesrunning the certain program.
 16. The computer implemented method ofclaim 9, wherein the determining provisioning resources for the certainvirtual machine includes determining predicted disk utilization resourceneeds of the certain virtual machine based on a growth rate of thecertain virtual machine determined using a historical disk utilizationgrowth rate of the multiple virtual machines.
 17. A computer implementedmethod comprising: predictively provisioning cloud computing resourcesof a cloud computing environment for at least one virtual machine; andtriggering initializing the at least one virtual machine with theprovisioned cloud computing resources of the cloud computingenvironment, wherein the predictively provisioning cloud computingresources of a cloud computing environment for at least one virtualmachine includes predictively provisioning a certain virtual machine forrunning a certain program, wherein the triggering initializing the atleast one virtual machine includes triggering initializing the certainvirtual machine with provisioning resources for the certain virtualmachine, the provisioning resources for the certain virtual machinebased on a growth rate of the certain virtual machine, wherein thegrowth rate of the certain virtual machine is determined using ahistorical growth rate of multiple virtual machines running the certainprogram.
 18. The computer implemented method of claim 17, wherein thetriggering initializing the at least one virtual machine includestriggering initializing the certain virtual machine with theprovisioning resources for the certain virtual machine, wherein thedetermining provisioning resources for the certain virtual machineincludes determining predicted resource utilization needs of the certainvirtual machine based on historical resource utilization of the multiplevirtual machines.
 19. The computer implemented method of claim 17,wherein the triggering initializing the at least one virtual machineincludes triggering initializing the certain virtual machine with theprovisioning resources for the certain virtual machine.
 20. The computerimplemented method of claim 17, wherein the determining provisioningresources for the certain virtual machine includes determining predictedresource utilization needs of the certain virtual machine based onhistorical resource utilization of the multiple virtual machines runningthe certain program.
 21. The computer implemented method of claim 17,wherein the determining provisioning resources for the certain virtualmachine includes determining predicted disk utilization resource needsof the certain virtual machine based on a growth rate of the certainvirtual machine determined using a historical disk utilization growthrate of the multiple virtual machines running the certain program. 22.The computer implemented method of claim 17, wherein the determiningprovisioning resources for the certain virtual machine includesdetermining predicted disk utilization resource needs of the certainvirtual machine based on a growth rate of the certain virtual machinedetermined using a historical disk utilization growth rate of themultiple virtual machines.
 23. A computer implemented method comprising:predictively provisioning cloud computing resources of a cloud computingenvironment for at least one virtual machine; and triggeringinitializing the at least one virtual machine with the provisioned cloudcomputing resources of the cloud computing environment, wherein thepredictively provisioning includes receiving historical utilizationinformation of multiple virtual machines of the cloud computingenvironment, the multiple virtual machines having similarcharacteristics to the at least one virtual machine, and determining thecloud computing resources for the at least one virtual machine using thehistorical utilization information of the multiple virtual machines;triggering, by the one or more processor, initializing the at least onevirtual machine with the provisioned cloud computing resources of thecloud computing environment; and wherein the predictively provisioningcloud computing resources of a cloud computing environment for at leastone virtual machine includes predictively provisioning a certain virtualmachine running a certain program, wherein determining provisioningresources for the certain virtual machine includes determining predictedresource needs of the certain virtual machine based on utilizationtrends of the certain virtual machine determined using a historicalutilization of machines of the multiple virtual machines running thecertain program.
 24. The computer implemented method of claim 23,wherein the triggering initializing the at least one virtual machineincludes triggering initializing the certain virtual machine with theprovisioning resources for the certain virtual machine, wherein thedetermining provisioning resources for the certain virtual machineincludes determining predicted resource utilization needs of the certainvirtual machine based on historical resource utilization of the multiplevirtual machines.
 25. The computer implemented method of claim 23,wherein the triggering initializing the at least one virtual machineincludes triggering initializing the certain virtual machine with theprovisioning resources for the certain virtual machine.